In accordance with the increasing demands for high-speed and high-quality data transmissions, a Multiple-Input Multiple-Output (MIMO) wireless communication system using a plurality of transmit antennas and receive antennas is attracting much attention as a technique to meet those demands. MIMO technology performs communications using a plurality of streams via the antennas to thus greatly enhance the channel capacity compared to a single-antenna system. For example, when the transmitting end and the receiving end employ M-ary transmit antennas and M-ary receive antennas respectively, channels of the antennas are independent of each other, a bandwidth and a total transmit power are fixed, and an average channel capacity increases by M times the single antenna.
To maximize the performance of MIMO technology, a MultiUser (MU) MIMO technique is suggested to transmit signals to multiple users over the multiple transmit antennas at the same time. The MU MIMO technique achieves all of a spatial diversity gain, a spatial multiplexing gain, and an MU diversity gain. Thus, the MU MIMO is receiving much attention as the technique for maximizing the gain of a MIMO system.
Detection methods for maximizing the performance of the MIMO technique include Maximum Likelihood (ML) detection. The ML detection detects a signal in the unit of a vector by considering a transmit signal vector including a plurality of streams as one unit. Accordingly, when per stream channel information is generated, a Modulation and Coding Scheme (MCS) level (i.e., a modulation order) of each stream affects an effective channel quality of the other streams.
To determine the MCS level of the transmitting end, the receiving end needs to feed back per stream channel quality information. In a single user mode in a system using ML detection, all of the streams are allocated to one receiving end. There is no inconsistency between the channel information fed back by the receiving end and the MCS level determined by the transmitting end. Consequently, the receiving end is able to predict the MCS level of every stream and is able to generate accurate channel quality information. By contrast, in the MU mode, the streams are distributed to the multiple receivers. Accordingly, the MCS level of each stream is determined after the stream allocation is completed. The receiving end does not know which MCS level the transmitting end determines until the stream allocation is completed. In view of the receiving end using ML detection, the unknown MCS level of some streams implies that it is impossible to generate the channel quality information of the other streams.
As discussed above, since the streams are distributed to multiple receiving ends in the MU mode of the MIMO system, the receiver using ML detection cannot generate the per stream channel quality information. The transmitting end needs to determine whether to enter the MU mode or the single user mode. For doing so, the receiver should feed back proper channel information regarding the two modes. To this end, in a MIMO wireless communication system using ML detection, a method for generating the channel information for the MU mode, constituting the feedback information, and determining the mode using the feedback information is needed.